Methods trained with adversarial loss (SinGAN [1]}, ESRGAN\(^+\)A larger version of ESRGAN with similar FLOPs to GLEAN. [2]}) can preserve the local structures, but fail in synthesizing convincing textures and details.
Specifically, SinGAN fails to capture the natural image style, producing a painting-like image. Although ESRGAN\(^+\)  is capable of generating a realistic image, it struggles to synthesize fine details and introduces unnatural artifacts in detailed regions. It is worth emphasizing that although ESRGAN\(^+\)  achieves competitive results on human faces, its performances on other categories such as cats and cars are less promising (see Fig. REF  and Fig. REF ).
With the latent bank providing natural image priors, GLEAN succeeds in both fidelity and naturalness.
For example, when compared to ESRGAN\(^+\) , GLEAN reconstructs eyes with better shape and details.
We further extend our method to larger scale factors in Fig. REF . GLEAN successfully generates perceptually convincing images resembling the ground-truth for up to \(64{\times }\)  upscaling.
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